Perspective on essential information in ...
Document type :
Article dans une revue scientifique: Article original
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Title :
Perspective on essential information in multivariate curve resolution
Author(s) :
Ruckebusch, Cyril [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Ghaffari, M. [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Hugelier, Siewert [Auteur]
Department of Chemistry [Leuven]
Omidikia, N. [Auteur]
Department of Chemistry

Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Vitale, Raffaele [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement (LASIRE) - UMR 8516
Ghaffari, M. [Auteur]
Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 [LASIRE]
Hugelier, Siewert [Auteur]
Department of Chemistry [Leuven]
Omidikia, N. [Auteur]
Department of Chemistry
Journal title :
TrAC Trends in Analytical Chemistry
Abbreviated title :
TrAC Trends in Analytical Chemistry
Volume number :
132
Pages :
116044
Publication date :
2020-11
ISSN :
01659936
HAL domain(s) :
Chimie/Chimie théorique et/ou physique
English abstract : [en]
We propose to take a new perspective on the construction and interpretation of multivariate curve resolution (MCR) models for the decomposition of spectral mixture data. We start by introducing archetypes, i.e. points that ...
Show more >We propose to take a new perspective on the construction and interpretation of multivariate curve resolution (MCR) models for the decomposition of spectral mixture data. We start by introducing archetypes, i.e. points that approximate the convex hull of a data cloud and correspond to the most linearly dissimilar observations. Identifying archetypes is a way to select essential samples (ESs) and essential variables (EVs) of a data matrix before MCR decomposition. Working with ESs and EVs, we then identify three main implications. The first is data reduction, which brings simplicity and computational speed. The second is prioritization, with the ESs and EVs profiles being the most dominant features to solve the MCR problem. The third is interpretability: the reduced data sets provide more direct insights and better understanding of final MCR models. Overall, the selection of ESs and EVs offers new opportunities that are worth being explored.Show less >
Show more >We propose to take a new perspective on the construction and interpretation of multivariate curve resolution (MCR) models for the decomposition of spectral mixture data. We start by introducing archetypes, i.e. points that approximate the convex hull of a data cloud and correspond to the most linearly dissimilar observations. Identifying archetypes is a way to select essential samples (ESs) and essential variables (EVs) of a data matrix before MCR decomposition. Working with ESs and EVs, we then identify three main implications. The first is data reduction, which brings simplicity and computational speed. The second is prioritization, with the ESs and EVs profiles being the most dominant features to solve the MCR problem. The third is interpretability: the reduced data sets provide more direct insights and better understanding of final MCR models. Overall, the selection of ESs and EVs offers new opportunities that are worth being explored.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
CNRS
ENSCL
Université de Lille
ENSCL
Université de Lille
Collections :
Research team(s) :
Dynamics, Nanoscopy & Chemometrics (DyNaChem)
Submission date :
2021-12-08T09:53:14Z
2024-02-14T07:24:49Z
2024-02-14T07:24:49Z